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Throughout the 2021-22 academic year, we will be prioritising face to face teaching as part of a blended learning approach that builds on the lessons learned over the course of the Coronavirus pandemic. Teaching will vary between online and on-campus delivery through the year, and you should read guidance from the academic department for details of how this will work for a particular module. You can find out more about the University’s overall response to Coronavirus at: https://warwick.ac.uk/coronavirus.

WM9A9-15 Big Data, Analytics & Optimisation

Department
WMG
Level
Taught Postgraduate Level
Module leader
Liping Zheng
Credit value
15
Module duration
2 weeks
Assessment
Multiple
Study locations
  • University of Warwick main campus, Coventry Primary
  • Distance or Online Delivery
Introductory description

Advanced eCommerce and Digital Analytics involves the utilisation of many of the newer, and more sophisticated technologies and techniques for optimising digital assets and business processes. This module introduces some of the most important of these, and gives participants practical experience of their uses

Module aims

The module aims to expose students to the latest in technical eCommerce practice and provide a toolkit for the implementation and optimisation of digital platforms and strategies. This incorporates technological developments, strategy and management, as well as analytical methods to derive insights from data at scale (which is common to modern digital platforms). Participants will get the opportunity to develop hands-on experience with the latest technology, within a modern cloud environment, to critically analyse a range of business scenarios, and implement sophisticated big data and digital analytics solutions

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

  1. eCommerce optimisation
  • Google analytics
  • Multivariate testing
  • Technical SEO
  • Personalisation
  • Chatbots
  1. Big data
  • Big data fundamentals
  • NoSQL databases and data lakes
  • Internet of Things
  • Artificial intelligence and machine learning
  1. Social media analytics
  • Natural language processing
  • Social network analysis
  • Sentiment analysis
  • Topic models
  • Image processing
  1. Data visualisation
  • Interactive data visualisation
  • Dashboards
  1. A practical simulation of the above topics
Learning outcomes

By the end of the module, students should be able to:

  • Demonstrate a comprehensive understanding of the key differences between Big Data technologies and analysis methods and traditional approaches.
  • Evaluate real-world scenarios and devise appropriate analytical solutions.
  • Demonstrate a comprehensive understanding of the core concepts of visual communication and data visualisation.
  • Practically implement analytics and optimistaion techniques in real-world settings
Indicative reading list

View reading list on Talis Aspire

Interdisciplinary

A mixture of technology/computing topics and business topics

International

Topics are of high international demand

Subject specific skills

Big data, analytics, visualisation, multivariate testing, technical SEO, social media analytics

Transferable skills

Programming, statistics and modelling, team work, critical analysis

Study time

Type Required
Lectures 14 sessions of 1 hour 30 minutes (14%)
Seminars 4 sessions of 1 hour 30 minutes (4%)
Supervised practical classes 12 sessions of 1 hour 30 minutes (12%)
Assessment 105 hours (70%)
Total 150 hours
Private study description

No private study requirements defined for this module.

Costs

No further costs have been identified for this module.

You do not need to pass all assessment components to pass the module.

Assessment group A
Weighting Study time
Big Data Analytics Presentation 20% 15 hours

A presentation of analyses of various datasets and recommendations on business actions from them

Post Module Assignment 80% 90 hours

A business-style report discussing core topics in big data and eCommerce optimisation

Assessment group R
Weighting Study time
Post Module Assignment 100%

A business-style report discussing core topics in big data and eCommerce optimisation

Feedback on assessment

Verbal feedback for in-module element. Written feedback and annotated scripts for post-module element

There is currently no information about the courses for which this module is core or optional.